Bayesian sparse channel estimation

Chulong Chen; Michael D. Zoltowski

doi:10.1117/12.919302

3 May 2012 Bayesian sparse channel estimation

Chulong Chen, Michael D. Zoltowski

Proceedings Volume 8404, Wireless Sensing, Localization, and Processing VII; 84040A (2012) https://doi.org/10.1117/12.919302
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.

Citation Download Citation

Chulong Chen and Michael D. Zoltowski "Bayesian sparse channel estimation", Proc. SPIE 8404, Wireless Sensing, Localization, and Processing VII, 84040A (3 May 2012); https://doi.org/10.1117/12.919302

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